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Journal of Plant Protection in the Tropics

Integrating Crop and Pest Management : the need for comprehensive

management of yield constraints in cropping systems

P. S. Teng,

Department of Plant*Pathology,

University of Minnesota, St. Paul, MN 55108, U.S.A.

Abstract: Pests continue to cause significant crop losses in many

cropping systems in spite of available technology for their control.

Because crop management and pest management have too often been

viewed as separate activities, little true integration has occurred.

The IPM concept is not being 'ide1y applied even though there exists

much knowledge on crop loss assessment, systems analysis, systems

modelling, individual pest sciences and pest management. A systems

analysis framework is described to identify areas for integrating

crop and pest subsytems, and for integrating biological knowledge,

management knowledge and social/environmental considerations. This

framework will facilitate the design of integrated crop-pest

management programs, leading to a rational allocation of resources in

the activities of knowledge generation (research), dissemination

(extension), modification and reception.

Keywords: Integrated Crop-Pest Management, IPM, Systems Approach.

Author citation: Teng, P.S.

INTRODUCTION

Next to the physical environment, pests are the most important

constraints to incleased crop yields in many parts of the world. The

common techniques to deal with yield losses caused by pests have been

geneti2cal (host plant resistance), chemical, biological and cultural.

A common scenario is often to use host plant resistance as a first

line of defence where possible, followed by the use of chemicals

(Robinson, 1976). For example, the major impact of the international

agricultural center system sponsored by the Consultative Group on

International A-riculcural Research (CGIAR), on plant protection, has

been the incorporation of pest resistance into improved genotypes

(Technical Advisory Committee, 1979). Biological and cultural

methods, though appealing have been limited in their use worldwide.

The conceptual basis for much of the agricultural pest control in the

developed world has been integrated Pest Management (IPM) since the

late 1960's. The IPM concept grew out of a need to respond to

developiug public concern about indiscriminate pesticide use in the

1960's (Huffaker and Smith, 1980), and the resultant development of

resistance in pest populations to chemicals. The major application

of the concept was initially limited to reduction in pesticide use

and a deployment of alternate control techniques, although the IPM

concept itself proposes an ideal - of rational use of control inputs

to maintain or increase yields at minimal risk to the environment and

society, yet giving a profit to the farmer (FAO, 1984a). The

rational use of multiple control techniques is strongly advocated in

writings dealing with the concept, yet recently, Tschirley (1984) has

questioned whether integration has been successfully achieved.

At the same time that the IPM concept was winning wide acceptance

worldwide, several other conceptual approaches were being developed

that affected IPM. These were the concepts of on-farm production

constraints research as pioneered by the International Rice Research

Institute (de Datta, 1978; IRRI, 1979), and the Farming Systems

Research approach to solving problems in developing nations, actively

promoted by the US Agency for International Development. Both of

these approaches highlight an increasing awareness among agricultural

scientists that real world problems are not compartmentalized into

scientific disciplines (Spedding, 1979; Dent, 1978; Russell et al.,

1982), and that institutional barriers are very evident when

attempting to solve development problems in the third world.

Concurrently, there has been increased appreciation of the limited

role that technology can play to narrow the "yield gap" in some

cropping systems; that technology and research are only two of the

many essential ingredients in improving crop productivity (Norton,

1982b; Norton and Mumford, 1983a). Further, the IPM experience has

sensitized the scientific community to the delicate nature of

agricultural ecosystems, and to the need to anticipate how changes

affecting one aspect of an agricultural system has repurcussions on

other aspects as well as on total system behaviour (Teng, 1982;

Ruesink, 1976). The scientific community is at the same time being

forced into thinking more about interdisciplinary research

(Oldenstadt et al., 1982; Russell et al., 1982). The forces behind

this resemble many of the concepts inherent in the "Systems Approach"

(Dent, 1978). Farming Systems Research and the IPM concept both have

similarities to the systems approach. It is the major premise of

this paper that the conceptual framework for solving complex

agricultural problems in general, and crop-pest problems in specific,

is the approach commonly called the "systems approach".

Some of the basic ideas in the systems approach are the hierarchical

nature of systems (e.g. crop-pest ecosystem), their division into

subsystems (e.g. crop, pest), the interrelatedness between the

subsystems, and how addressing only part of the system (e.g. pest or

crop) will not lead to a complete understanding of the system for its

management (Teng, 1985). In agriculture, we see crop scientists or

agronomists emphasize the crop, pest scientists emphasize the pest,

and so on. The IPM concept was an early attempt to recognize the

interrelatedness between the different pest, parasite and predator

populations that make up agricultural ecosystems, and a systems

approach to IPM has been advocated (Ruesink, 1976). The argument

presented here is that, because of "disciplinary myopia", in both the

developed and developing countries, no real holism or integration has

occurred in treating the crop and its associated biological

populations as equal subsystems. This latter treatment is necessary

for a strong integration, and a move must be made to get away from

the situation where an IPM person emphasizes the pest subsystems

while the agronomist emphasizes the crop subsystem.

There is an urgent need to integrate both crop management and pest

management, to treat these as equally important activities, and to

synthesize both conceptually into an activity of integrated crop-pest

management.

The remainder of this paper will be used to review the knowledge base

available for implementing the concept and to discuss the design of

integrated crop-pest management programs using the concept.

THE KNOWLEDGE BASE FOR IMPLEMENTING INTEGRATED CROP-PEST MANAGEMENT

Systems Analysis

The systems approach is a scientific philosophy that proposes a

holistic view on agricultural systems (Spedding, 1979). In practice,

the philosophy is actioned using a set of methodologic tools,

commonly referred to as "systems research" (Dent and Blackie, 1979).

This in turn comprises "systems analysis" and "systems synthesis"

(Teng, 1982). Conducting a systems analysis of any management or

biological system starts with a conceptual analysis of the system,

the physical components of the system structure, the components of

its operating environment, the inter-relationships between structural

and environmental components, rules governing system behaviour and

system output (Teng, 1985). Systems analysis does not require more

than pencil, paper and a knowledge of how to conduct the analysis. A

tangible product of this mental exercise is a conceptual model of the

system being studied, its major components and their relacionships to

each other and to the environment. Following these activities, and

using the ccncertual model as a framework, a literature review or

interaction with scientists knowledgeable about the system will lead

to identifyin., knowledge gaps about the system (Heong et al., 1984;

Kranz and Hau, 1980). This relatively informal approach to analysis

has proved to be a very educational tool in designing management

programs for specific pests or crops (Giese et al., 1975; FAO, 1984b;

Norton, 1982a), for identifying research/extension needs for IPM

(Johnson et al., 1985b), and for evaluating payoffs to development

projects (National Research Council, 1982). The systems analysis

procedure described above has also been called a "soft systems

approach", in contrast with computer modeling, which is a "hard

systems approach" (Bawden et al., 1984).

The systems analysis of any farming system will also define the

spatial and temporal hierarchy of the system. For example, a potato

farming system may consist of many distinct potato production

systems, each occurring on a separate production area (Johnson et

al., 1985b). Further, the analysis extends not only to the ecosystem

and management system, but also to the flow of information within it.

This information flow occurs from those who generate information for

improving the system (the researchers), to those who synthesize the

information (managers, modelers), to those who disseminate the

information (extension personnel), till finally to the information

receptor (farmer) (Norton, 1982a). A systems analysis can therefore

lead to identifying which aspect of the information flow (research,

synthesis, extension, reception) needs to be improved in order that

infrastructural constraints to increased crop productivity be

reduced. The technique is well documented in scientific literature

(Dent & Anderson, 1971; Kranz and Hau, 1980; Teng, 1982, 1985;

Zadoks, 1971).

A rigorous systems analysis is necessary before any knowledge is

generated for an integrated crop-pest management system.

Systems Modeling

In many developed countries, it has become fashionable to build

complex and detailed computer models of crops, pests and farms.

These models attempt to synthesize multiple hypotheses and data sets

from different laboratories, in order that the behaviour of the

system be simulated (Dent and Blackie, 1979). Although systems

analysis is an essential step in the process of building a system

simulation model, many scientists have recognized that the process of

systems analysis may be as beneficial if not more, than t'- final

model itself (Giese et al., 1975; Teng, 1985). In the context of

third world agriculture, systems models may have a role in testing

strategies for system improvement, especially if models already exist

in the developed countries and only environmental data are required

from elsewhere to run the models. Examples are the physiological

crop growth models for wheat, sorghum, corn, etc. (Loomis et al,

1979), which may be used to identify physiological constraints on

yield in developing countries. The knowledge base and technology

needed to develop these models makes it very inefficient for

developing country scientists to attempt building their own models.

Apart from detailed crop physiological models, simpler models exist

for predicting crop yield (Johnson et al. 1985a; Fishman et al.,

1984), pest population growth (Teng, 1985), crop yield losses due to

pests (Teng and Shane, 1984) and for decision-making in pest control

(Teng and Rouse, 1984). All these require some form of computer

technology, the minimum of which is a microcomputer, a piece of

equipment by no means impossible to obtain in many developing

countries.

The procedures for building computer-based simulation models of crops

and pests are now fairly well-established (Ruesink, 1976; Loomis and

Adams, 1983; Teng, 1985). These models have shown themselves useful

research tools - to test hypotheses, to synthesize information and to

explore management strategies. There has however only been limited

success in the use of computer models for on-farm crop-pest

management, although there is much activicy underway in many

developed countries (Teng and Rouse, 1984). Simple mathematical

models with relatively few equations have been used for yield and

loss prediction in the developed countries, and these may be the type

of models with quick payoff for the developing countries, rather than

the detailed computer-based models (Seem and Russo, 1984).

Systems analysis (the "soft" systems approach) and systems modeling

(the "hard" systems approach) have demonstrated their usefulness for

developing practical methods to guide farmer decision-making. In

plant pathology, use of economic decision-aids such as those for

soybean disease control (Stuckey et al., 1984) exemplify a "soft"

approach. In this soybean decision-aid, knowledge on the system is

synthesized into numerical values, so-called "points", e.g. in the

Illinois system, if rainfall is below normal, the point value equals

0, it rainfall is normal, the point value equals 2 and if rainfall is

above normal, the point value equals 4. Points are assigned for

different states of cropping history, cultivar type, etc. and a total

of 15 or more is used to trigger a spray action in grain fields.

Similarly, systems analysis was used to identify questions with

numerical answers (0 or _.), which together with simple damage

functions are used in the BEANRUST (Meronuck and Teng, 1984) and

RUSTMAN (Teng and Montgomery, 1982) programs to time spraying fir

rust in beans and sweet corn respectively. Chiarappa (1974)

developed a flow chart of questions to help determine if any

pathosystem would be amenable to the use of supervised disease

control techniques, in particular, quantitative predictive models.

The use of a "soft" systems approach is adequate in many situations

for integrating known control measures for single or multiple pests

and should be used before any effort to develop complex models.

Crop Loss Assessment

Crop loss assessment has become a scientific theme which encompasses

many techniques (Zadoks, 1985). It may be viewed as a problem

definition discipline, providing the necessary information for

assessing and evaluating system performance. With respect to crop

and pest management, crop loss information allows decision-making on

the economics of pest control in individual farms, while at a farming

system level, crop loss information provides a basis for decisions on

the impact of new crop production/pest control technology, e.g.

pesticides (Teng and Shane, 1984).

Crop loss technology is useful for quantifying the magnitude of

production constraints due to similar pest (insect, disease, weed)

groups or to interacting pest populations (Walker, 1983). There are

many successful applications of the technology for gathering data

that can then be used to set research or extension priorities for

reducing the effect of production constraints (Pinstrup-Andersen et

al., 1976; Stynes, 1980; Wiese, 1982) . Essential components of

applying crop loss assessment for improving crop yields are the

estimation of attainable yield in the absence of pests, and the

estimation of the actual yield, when pests are present. There have

been significant advances made in the practical estimation of both

these yield levels, especially through the use of simple crop yield

models (Johnson et al., 1985; Fishman et al., 1984) and soil

moisture level measurements (Brown et al., 1981). A project in

Montana, U.S.A. used sample-surveys to demonstrate to farmers the

difference between wheat yields, with and without pest-induced losses

under water-stressed conditions, and could calculate for each farm a

biological production efficiency (Nissen and Juh:.ke, 1984).

As a scientific theme, crop loss assessment includes techniques for

the measurement of pest populations, pest abundance and pest injury,

sampling methods for estimating pest intensities in farmers' fields,

survey methods and on-farm pest-loss experiments (Teng and Shane,

1984). These techniques are applicable under both high technology

and low technology situ:,tions. In the developed countries, various

computer-based techniques - database management systems, information

delivery systems and economic decision aids for pest control - are

now commonplace (Teng and Rouse, 1984). As yet these have limited

use in improving farming practices in many developing countries.

However: computer technology has a definite role in increasing the

productivity of individual scientists and the efficiency of data

analysis in both developing and developed countries, especially

through the use of small microcomputers (Rouse and Teng, 1984)

Crop loss assessment offers a means for obtaining the information

whereby the importance of different pests on crop productivity can be

determined. Crop loss information in many countries will lead to

rational decisions on the allocation of scarce resources (James and

Teng, 1979). It is therefore an essential component of integrated

crop-pest management, whether the unit to be managed is a single

field or an agricultural system with many fields.

Pest Science

The component sciences in pest control have traditionally been

entomology, plant pathology and weed science (Allen and Bath, 1980).

Accompanying the development of these sciences has been a visible

strengthening of disciplinary departments in many research and

educational institution3 worldwide. While a valid argument can be

made for the need to have strong disciplinary departments in order to

further each science, an adverse effect has been that pest

management, which requires an interdisciplinary approach, has

suffered because of disciplinary barriers in institutions (Russell et

al., 1982). In the developed countries, much of the pest biologic

knowledge is available, although distinct gaps have been identified

even on crops that have been well-researched. For example, with

potatoes, a recent study (Johnson et al., 1985b) demonstrated the

lack of information on the following aspects of many pests: disease

epidemiology, pest dispersal, pest survival, predictive systems,

quantitative pest effects on yield, management thresholds, models for

explaining multiple pest-crop interactions, pest assessment methods,

pest sampling procedures and the regional distribution of pests. In

the developing countries, knowledge on the above and on more

fundamental pest biology such as life-cycles may even be incomplete.

The same study (Johnson et al., 1985b) showed that knowledge on pest

biology at the levels of organization of the individual, population

and community are more relevant for pest management than knowledge at

the levels of the cell or organelle. Another study on rice pest

management supports this conclusion (Norton, 1982a). A systems

analysis, done to identify what aspects of pest biology are required

to address specific management problems, will facilitate the

deployment of scarce research resources.

Pest Management Science

Pest management systems may be divided into the technical, biologic~al

system (supported by knowledge generated in the pest sciences) and

the management system (supported by knowledge from pest management

science) (Norton and Mumford, 1983a,b). Too often, biologic

knowledge has been generated without due consideration for the

decision-making process of the farmer, his managerial skills and

options, and the technology to implement the knowledge. Concurrent

with research on pest biology must be research on aspects such as the

integration of two or more management tactics for single or multiple

pests, the management of resistance genes in the crop population to

pests and in the pest population to chemicals, crop-pest ecosystem

design, the socio-economics of IPM implementation, the determination

and modification of curient farmer practices (Matteson et al.,

1984), the design of macro-level (regional) crop-pest management

strategies and the decision process of the farmer (Johnson et al.,

1985b). Furthermore, little has been done to research the management

aspects of the crop - pest system (a bioeconomic system) or the

economics of management (Mumford and Norton, 1984), when compared to

what is known on the management of industrial systems. This is

especially true in the use of quantitative management tools, such as

those known collectively as "Operations Research". For example, the

use of matrices to describe knowledge gaps on different components of

the crop-pest system (see section on System Description, below) is an

application of both the 'PERT' and 'critical path' methods commonly

used in R & D planning and management in the private sector (Ackoff

and Sasieni, 1968).

Pest management science is a relatively new endeavour even in the

developed countries (Brader, 1979) with innumerable problems in its

application, foremost of which are the strong disciplinary boundaries

at universities, research institutions and extension agencies. It

may well be that the developing countries, with a shorter history of

scientific institutionalization, are more amenable to adopting the

systems approaches required for effective implementation of

integrated crop-pest management.

DESIGN OF AN INTEGRATED CROP-PEST MANAGEMENT SYSTEM

The "soft" systems approach to program design for integrated

crop-pest mangement is represented in Fig. 1. An existing crop-pest

management system may be viewed as receiving input (from system

description and analysis), and providing output to enable system

improvement via some defined objectives. The output from system

description takes the form of problems which are identified and

questions which are raised; both lead to the generation of a

knowledge base via research or technology adaptation, from which

implementation steps may be taken. The improved crop-pest management

system is also viewed as having identifiable objectives which are

suited for evaluation through a monitoring / surveillance system,

thereby providing adequate feedback to determine if corrective action

is needed.

System description

The first step in design is to describe the crop-pest system by

detailing its biological system and its management system, using the

systems analysis method sensu Norton (1982a,b) and Johnson et al.

(1985). This phase involves defining the components of the major

biological subsystems, e.g. crop subsystem - species, cultivars ;

pest subsystem - individual pests, parasites and predators. For each

subsystem-subsystem combination (e.g. crop 1 - pest 1), a series of

analysis matrices is created, to identify the effect of weather, the

effect of pesticides, interactions between weather and pesticides,

etc. (Fig. 2). For example, in the weather matrix, the columns would

be different weather factors like temperature, relative humidity

while the rows would be the life cycle stages of each pest. Each

weather factor - life cycle stage "box" would then be filled in with

a notation indicating the state of knowledge, vis-a-vis importance

for pest management. Similarly, the change in state of the major

components of the system is traced through a time profile matrix,

which allows the identification of critical times for several states

(Walker et al., 1978; Norton, 1982a; Johnson et al., 1985b). This

kind of analysis also generates questions in the minds of the

part cipating scientists as to how important various aspects of the

crop-pest biological system are. The final product is a detailed

description of the crop-pest system, and is really a conceptual model

of the entire crop-pest ecosystem which can be used for guiding data

collection and further research, leading if desired to the

development of a detailed system simulation model.

The description of the management system includes analyzing the

decision process of the farmer by using a decision-tree to structure

all the decisions he/she encounters before, during and after each

"season" (Norton, 1982b; Norton and Mumford, 1983a). Each decision

can then be evaluated relative to potential value for crop or pest

management. A monetary value may also be assigned to each decision

point and an enterprise budget created for estimating the

cost-benefit of any management decision, its associated risk, etc.

(Johnson et al., 1985b). A payoff matrix is also created from this

analysis of farmer decisions, to help in estimating potential benefit

from conducting research on any decision point. Crop loss

information and damage functions are used at this point to enable

judgements on the potential importance of a pest.

Apart from describing the crop-pest system, it is also necessary that

the infrastructure for conducting research, for extension, for local

training of trainers, etc. be described and analyzed for potential

improvement.

Defining problems to be resolved

The system description step results in a determination of the major

components of the system, their linkages and their role in the

decision process of the farmer. it also defines hcw much is

currently known on all aspects of the crop-pest system, and what the

knowledge gaps are. Following the system description, it is

necessary to determine how much of the current research, extension

and teaching effort is addressing the knowledge gaps involved in the

biology/management of the system. At theo same time, an effort has to

be made to involve the scientists in a common institution (e.g.

within a province or a country), in a participatory process of

defining the constraints on system improvement and which of these

constraints deserve priority for action. The final product of this

step is to secure an agreement, through participation of all

scientists, of what the major crop-pest biologic or management

problems are, how they should be addressed, and what the action

timetable is (Johnson et al., 1985b). Often, several iterations of

opinion are necessary before agreement is possible on research

problems and needs, and the 'Delphi Method' (Linstone and Turoff,

1975) is one procedure used for this process (Johnson et al., 1985b).

All these should be done within the framework created in the system

description step. As Ruttan (1982) noted, research resource

allocation cannot be efficiently done by administrators, independent

of the opinions of researchers. As with a computer modeling effort,

the end users of the model need to be involved in the

conceptualization and development of the model, if the model is to

eventually have practical value (Dent and Blackie, 1979).

Researchers of the crop - pest management system need to be actively

involved in the definition of problems and the identifying of

research needs and priorities. In this context, information on the

occurrence of crop management problems, the magnitude and recurrence

of pest problems, data on production constraints obtained through

on-farm surveys or experiments, would all serve to help problem

definition. A listing of priorities for research, extension,

teaching, (and training in the case of developing countries) is one

end product of the problem definition step.

Setting objectives and timetables for implementing and

evaluating system improvement via research, extension and teaching.

In applying the systems approach to integrated crop-pest management,

it is important that the objectives for distinct activities be

clearly defined for known time periods. A periodic review is needed

of these objectives, the success or otherwise in meeting them and

their relationship to the overall goal of improving the crop-pest

management system. Within the context of the preceeding steps, a

framework can be created for linking traditional, basic and applied

research approaches to the systems research process (Dent and

Blackie, 1979). Again, this may take the form of qualitative or

quantitative models (Fig. 2), either of which will contain the

syntheses of information required for implementing a new production

and protection system (FAO, 1984b).

This step generates much of the information for system improvement.

Implicit in this is the generation of information through research,

the synthesis of the information, its dissemination and an evaluation

of the acceptibility of the "new" knowledge by farmers (Norton,

1982a). A continuing process of data gathering to provide feedback

on system status or improvement would be very useful, using concepts

and techniques for regional crop loss programs and large-area crop or

pest surveillance programs (James and Teng, 1979; Heong, 1983; Teng,

1984; Welch, 1984). It is important that a regular review process be

designed at the outset to examine the rate at which system objectives

are being met, and where necessary to repeat the analysis step. This

latter was one of the key points made by consultants who designed two

recent FAO projects, the Gambia Integrated Crop Management Project

and the Sudan Cotton Integrated Pest Control Project (FAO, 1984b).

However, it is difficult to expect the availability of an adequate

database for evaluating system behaviour without implementing a

large-area crop or pest surveillance system.

CONCLUDING REMARKS

Agricultural research worldwide is under serious resource

constraints, which in recent years has led to a need for greater

accountability in scientific effort. One by-product of this

situation is the emphasis on solving "real-world" problems, many of

which are short-term in nature and involve applied research. At the

same time, these "real-world" problems commonly recognize no

scientific, disciplinary boundaries and have required an

interdisciplinary team approach for their solution (Russell et al,

1982). Many of the reasons that are fuelling the move towards team

efforts at problem solving resemble ideas inherent in the systems

approach (Dent, 1978; Dent and Anderson, 1971). It has therefore not

been surprising to see increased application of systems methodology

in many diverse fields of agricultural research. However, what has

been less frequent, but which has potentially greater payoff, is use

of the systems approach to analyse, plan and evaluate projects. In

this paper, an effort has been made to show how the knowledge base

for integrated crop-pest management projects could benefit by using

an organizational framework derived with systems analysis.

Goodell (1984) recently posed a challenge : "Do we really want IPM to

work?", and rightly concluded that farmers in developing countries

often have technology thrust on them by scientists with little

appreciation for the technology needs of farmers. Wherever they have

been given the opportunity, farmers have been the only true

integrators of scientific knowledge. Although often viewed by

researchers as receipients of knowledge, farmers are by necessity,

also implicit practitioners of the "systems approach". It is sad

that the pest management scientific community, because of

disciplinary and institutional barriers, has only recently risen to

the twin challenges of firstly, interdisciplinary problem-solving and

secondly, doing research that matches the needs of the farmer.

Furthermore, disciplinary rivalries at many institutions often result

in independent research on crop management and pest management.

These issues are highlighted here because they have been major

impediments to ensuring there is relevant crop-pest management

research, and to the broad adoption of available knowledge on IPM.

The view presented in this paper is that a systems approach will

provide the basis to guide the research needed and to synthesize the

knowledge generated, for integrated crop-pest management.

ACKNOWLEDGEMENTS

I would like to thank G.A. Norton and J.D. Mumford, Imperial College,

London, and K.L. Heong, MARDI, Serdang for their useful discussions

on many of the ideas presented here. Richard J. Sauer and Vernon W.

Ruttan, University of Minesota made helpful comments on the

manuscript. This paper is from presentations made at the

Agricultural Rese.irch Policy Seminar, April 15-26 1985, Minneapolis,

U.S.A. and at the Workshop on Analysis and Control of Plant Disease

Epidemics, Chilanga, Zambia, February 24-29 1985. Any errors and

omissions remain solely the responsibility of the author.

REFERENCES

Ackoff, R.L. and Sasieni, M.W. (1968). Fundamentals of Operations

Research. New York : John Wiley. 455 pp.

Allen, G.E. and Bath, J.E. (1980) The conceptual and institutional

aspects of integrated pest management. BioScience 30 : 658- 664.

Bawden, R.J., R.D. Macadam, R.J. Packham and Valentine, I. (1984)

Systems thinking and practices in the education of agriculturalists.

Agricultural Systems 13 : 205-225.

Brader. L. (1979) Integrated pest control in the developing world.

Annual Review of Entomology 24 : 225 - 254.

Brown, P.L., A.L. Black, C.M. Smith, J.W. Enz and Caprio, J.M. (1981)

Soil water guidelines and precipitation probabilities for for barley

and spring wheat in flexible cropping systems in Montana and North

Dakota. Montana Cooperative Extension Service. Bulletin no. 356. 30

PP.

Chiarappa, L. (1974) Possibility of supervised plant disease control

in pest management systems. FAO Plant Protection Bulletin 22: 65

68.

de Datta, S.K., K.A. Gomez, R.W. Herdt and Barker, R. (1978) A

handbook on the methodology for an integrated experiment-survey on

rice yield constraints. IRRI, Los Banos, Phillipines. 60 pp.

Dent, J.B. (1978) Crop disease management : a systems research

approach. In Epidemiology and Crop Loss Assessment, pp. 30/1-30/10

(ed. R.C. Close et al.). Lincoln College Press.

Dent, J.B. and Anderson, J.R. (1971) Systems Analysis in Agricultural

Management. Wiley.

Dent, J.B. and Blackie, M.J. (1979) Systems simulation in

agriculture. Applied Science Publishers. 180 pp.

F.A.O., U.N. (1984a) Integrated pest control : an introductory

statement from the FAO/UN Panel of Experts on Integrated Pest

Control. (mimeo.) FAO Plant Protection Service, Rome. 38 pp.

F.A.O., U.N. (1984b) Analysis and design of integrated crop

management programmes (with particular reference to Africa). FAO

Committee of Experts on Pest Control, Paper no. AGP: PEST/84/WP/3:4.

(mimeo.) FAO Plant Protection Service, Rome. 18 pp.

Fishman, S., H. Talpaz, M. Dinar, M. Levy, Y. Arazi, Y. Rozman and

Varshavsky, S. (1984) A phenomenological model of dry matter

partitioning among plant organs for simulation of potato growth.

Agricultural Systems 14 : 159-169.

Giese, R.L., R.M. Peart and Huber, R.T. (1975) Pest management : a

pilot project exemplifies new ways of dealing with important

agricultural pests. Science 187 : 1045-1052.

Goodell, Grace. (1984) Challenges to international pest management

research and extension in the third world : Do we really want IPM to

work? Bulletin of the Entomological Society of America 30(3): 18-26.

Heong, K.L. (1981) The uses and management of pest surveillance data.

Malaysian Agricultural Journal 52 : 65-89.

Heong, K.L., Norton, G.A., Kenmore, P.E., Kranz, J., Teng, P.S. and

Way, M.J. (1984) The practical use of systems analysis in crop

protection, with particular reference to food crops in the tropics.

Report of the Ad Hoc Task Force on Systems Analysis, FAO/UNEP Panel

of Experts on Integrated Pest Control. (mimeo.) 30 pp.

Huffaker, C.B. and Smith, R.F. (1980) Rationale, organization and

development of a national integrated pest management project. In New

Technology of Pest Control, pp. 1-24 (ed. C.B. Huffaker). John Wiley.

500 pp.

IRRI. (1979) Farm-level constraints to high rice yields in Asia

1974-77. IRRI, Los Banos, Phillipines. 411 pp.

James, W.C. and Teng, P.S. (1979) The quantification of production

constraints associated with plant diseases. Applied Biology 4

201-267.

Johnson, K.B., S.B. Johnson and Teng, P.S. (1985a) A simple potato

growth model for crop pest management. Agricultural Systems : In

Press.

Johnson, S.B., P.S. Teng, G.W. Bird, E. Grafius, D. Nelson and Rouse,

D.I. (1985b) Analysis of potato production systems and identification

of IPM research needs. Final Report of the Potato Task Force, NC-166

Technical Committee on Integrated Pest Management. 145 pp.

Kranz, J. and Hau, B. (1980) Systems analysis in epidemiology.

Annual Review of Phytopathology 18 : 67-83.

Linstone, H.A. and Turoff, M. (1975). The Delphi Method. Reading,

Massachusetts : Addison-Wesley. 620 pp.

Loomis, R.S. and Adams, S.S. (1983) Integrative analyses of

host-pathogen relations. Annual Review of Phytopathology 21

341-362.

Loomis, R.S., R. Rabbinge and Ng, E. (1979) Explanatory models in

crop physiology. Annual Review of Plant Physiology 30 : 339-367.

Matteson, P.C., M.A. Altieri and Gagne, W.C. (1984) Modification of

small farmer practices for better pest management. Annual Review of

Entomology 29 : 383-402.

Meronuck, R.A. and Teng, P.S. (1984) Economic thresholds for chemical

control of bean rust. Phytopathology 74 : 796 (Abst.).

Mumford, J. D. and Norton, G.A. (1984) Economics of pest control.

Annual Review of Entomology 29 : 157-174.

National Research Council. (1976) Systems Analysis and Operations

Research : A tool for policy and program planning for developing

countries. National Academy of Sciences, Washington D.C. 99 pp.

Nissen, S.J. and Juhnke, M.E. (1984) Integrated crop management for

dryland small grain production in Montana. Plant Disease 68

748-752.

Norton, G.A. (1982a) Report on a systems analysis approach to rice

pest management in Malaysia. MARDI Report. 62 pp.

Norton, G.A. (1982b) A decision-analysis approach to integrated pest

control. Crop Protection 1 : 147-164.

Norton, G.A. and Mumford, J.D. (1983a) Information gaps in pest

management. In Proceedings of the International Conferrence on Plant

Protection in the Tropics, pp. 589-597 (ed. K.L. Heong, B.S. Lee,

T.M. Lim, C.H. Teoh, Yusof Ibrahim). Malaysian Plant Protection

Society, Kuala Lumpur.

Norton, G.A. and Mumford, J.D. (1983b) Decision making in pest

control. Applied Biology 8 : 87-119.

Oldenstadt, D.L., R.E. Allan, G.W. Bruehl, D.A. Dillman, E.L.

Michalson, R.I. Papendick and D.J. Rydrych. (1982) Solutions to

environmental and economic problems (STEEP). Science 217 : 904-909.

Pinstrup-Andersen, P., N. de Londono and M. Infante. (1976) A

suggested procedure for estimating yield and production losses in

crops. PANS 22 : 359-365.

Robinson, R. (1976) Plant Pathosystems. Springer-Verlag. 184 pp.

Rouse, D.I. and Teng, P.S. (1984) Understanding computers - a useful

tool in plant pathology. Plant Disease 68 : 365-369.

Ruesink, W.G. (1976) Status of the systems approach to pest

management. Annual Review of Entomology 21 : 27-44.

Russell, M.G., Sauer, R.J. and Barnes, J.M. (Eds.). (1982) Enabling

Interdisciplinary Research : Perspectives from Agriculture, Forestry

and Home Economics. Miscellaneous Pub. no. 19, Agricultural

Experiment Station, University of Minnesota. 183 pp.

Ruttan, V.W. (1982) Agricultural Research Policy. Chapter 11 -

Research Resource Allocation. University of Minnesota Press. 369 pp.

Seem, R.C. and Russo, J.M. (1984) Simple decision aids for practical

control of pests. Plant Disease 68 : 656-660.

Spedding, C.R.W. (1979) An Introduction to Agricultural Systems.

Applied Science Publishers. 246 pp.

Stuckey, R.E., Moore, W.F. and Wrather, J.A. (1984) Predictive

systems for scheduling foliar fungicides on soybeans. Plant Disease

68 : 743 - 745.

Stynes, B.A. (1980) Synoptic methodologies for crop loss asessment.

pp. 166-75 in Teng & Krupa ref. (below).

Technical Advisory Committee, CGIAR. (1979) Report on Crop

Protection.

Teng, P.S. (1982) A lystems approach to plant disease epidemiology

and management. Agriculture and Forestry Bulletin, University of

Alberta 5 : 13-18.

Teng, P.S. (1984) Surveillance systems in disease management. FAO

Plant Protection Bulletin 32 : 51-60.

Teng, P.S. (1985) A comparison of simulation approaches to epidemic

modeling. Annual Review of Phytopathology 23 : 351-379.

Teng, P.S. and Krupa, S.V. (1980) Crop Loss Assessment. Miscellaneous

Publication no. 7, Agricultural Experiment Station, University of

Minnesota. 326 pp.

Teng, P.S. and Montgomery, P.R. (1982) RUSTMAN : a portable,

microcomputer-basp9 economic decision model for sweet corn rust

control. Phytopathology 72 : 1140 (Abst.)

Teng, P.S. and Rouse, D.I. (1984) Understanding computers -

Applications in plant pathology. Plant Disease 68 : 539-543.

Teng, P.S. and Shane, W.W. (1984) Crop losses caused by plant

pathogens. CRC Critical Reviews in Plant Sciences 2 : 21-47.

Tschirley, F.H. (1984) Integrated pest management. BioScience 34

69.

Walker, B.H., Norton, G.A., Conway, G.R., Comins, H.N. and Birley, M.

(1978) A procedure for multidisciplinary ecosystem research with

reference to the south african ecosystem project. Journal of Applied

Ecology 15 : 481-502.

Walker, P.T. (1983) Crop losses : the need to quantify the effects of

pests, diseases and weeds on agricultural production. Agriculture,

Ecosystems and Environment 9 : 119 - 158.

Wiese, M.V. (1982) Crop management by comprehensive appraisal of

yield determining variables. Annual Review of Phytopathology 20

419-432.

Welch, S.M. (1984) Developments in computer-based IPM extension

delivery systems. Annual Review of Entomology 29 : 359-381.

Zadoks, J.C. (1971) Systems analysis and the dynamics of epidemics.

Phytopathology 61 : 600-610.

Zadoks, J.C. (1985) Concepts in crop loss assessment. Annual Review

of Phytopathology 23 : in press.

4. - SYSTEM DESCRIPTION

& ANALYSIS

inu I IG

CURRENT SYSTEM jPROBLEMS DEFINED & QUESTIONS RAISED

Generation of knowledge base (Research/Adaptation)

IMPROVED SYSTEM Qualitative Quantitative Models Models

SYSTEM OBJECTIVES IMPLEMENTATION objectives, timetable evaluation

Fig. 1. Steps in the design of an Integrated Program for Crop - Pest Management using the Systems Approach.

STATIC DYNAMIC STATIC DYNAMIC COMPONENTS COMPONENTS COMPONENTS COMPONENTS

1.Key entities 1.Time profile 1.Enterprise l.Farmers' budgets Perceptions

2.Crop-pest 2.Crop Loss & Receptivity 2.Payoff

3.Weather Matrix 2.Decision tree

4.Pesticide 3.Decision Model

5.Predators, Parasites

6.Pest-pest interactions

7.Key pest,crop life cycles

8.Agronomy

Fig.2 Components of a soft systems approach for crop-pest management system analysis. CAfter Jor+ 1 o *I

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